Abstract
Parameters that vary monotonically with fault development are useful in condition monitoring, but not easy to find especially for complex systems. A method using fuzzy preference based rough set model and principle component analysis (PCA) is proposed to generate such an indicator. The fuzzy preference based rough set model is employed to evaluate the monotonic trends of features reflecting machinery conditions. PCA is used to condense the informative features and generate an indicator which can represent the development of machine health condition. The effectiveness of the proposed method is tested for damage level detection of an impeller in a slurry pump.
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Zhao, X., Zuo, M.J., Patel, T. (2010). Application of Fuzzy Preference Based Rough Set Model to Condition Monitoring. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_73
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DOI: https://doi.org/10.1007/978-3-642-13529-3_73
Publisher Name: Springer, Berlin, Heidelberg
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